2022
Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from a Data-Driven Perspective
Gu J, Dai J, Lu H, Zhao H. Comprehensive Analysis of Ubiquitously Expressed Genes in Humans from a Data-Driven Perspective. Genomics Proteomics & Bioinformatics 2022, 21: 164-176. PMID: 35569803, PMCID: PMC10373092, DOI: 10.1016/j.gpb.2021.08.017.Peer-Reviewed Original ResearchConceptsGlobal expression patternsHuman transcriptomeExpression patternsHuman genesTemporal gene expression patternsVaried expression patternsGene expression patternsInternal reference genesHuman genomeTranscriptomeRegulatory codeGene clusteringMolecular mechanismsReference genesStable expressionIslet beta cellsHuman diseasesGenesExpression measurementsBeta cellsUbiquitouslyPhysiological conditionsValuable resourceComprehensive characterizationExtensive collection
2019
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchMeSH KeywordsGene ExpressionGene Expression ProfilingGenome-Wide Association StudyGenotypeHumansModels, GeneticPolymorphism, Single NucleotideTranscriptomeConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2016
Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures
Zheng B, Liu J, Gu J, Du J, Wang L, Gu S, Cheng J, Yang J, Lu H. Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures. PLOS ONE 2016, 11: e0164570. PMID: 27776138, PMCID: PMC5077123, DOI: 10.1371/journal.pone.0164570.Peer-Reviewed Original ResearchConceptsClinical dataGene expression signaturesClinical informationMalignant thyroid nodulesThyroid nodulesExpression signaturesNovel diagnostic testsClinical characteristicsClinical featuresPreoperative diagnosisThyroid carcinomaThyroid tumorsPredictive sensitivityDifferent hospitalsDiagnostic testsThyroid samplesPatientsRegulation of CBL and ESR1 expression by microRNA-22-3p, 513a-5p and 625-5p may impact the pathogenesis of dust mite-induced pediatric asthma
Dong X, Xu M, Ren Z, Gu J, Lu M, Lu Q, Zhong N. Regulation of CBL and ESR1 expression by microRNA-22-3p, 513a-5p and 625-5p may impact the pathogenesis of dust mite-induced pediatric asthma. International Journal Of Molecular Medicine 2016, 38: 446-456. PMID: 27277384, PMCID: PMC4935459, DOI: 10.3892/ijmm.2016.2634.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAsthmaCase-Control StudiesChildCytokinesDemographyDown-RegulationErbB ReceptorsEstrogen Receptor alphaFemaleGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansInflammation MediatorsMaleMicroRNAsNF-kappa BPhosphatidylinositol 3-KinasesProto-Oncogene MasProto-Oncogene Proteins c-aktProto-Oncogene Proteins c-cblPyroglyphidaeReal-Time Polymerase Chain ReactionRNA, MessengerSyk KinaseUp-RegulationConceptsPediatric asthmaPlasma concentrationsGender-matched healthy controlsCbl proto-oncogeneMite-induced asthmaPeroxisome proliferator-activated receptorInflammatory cytokine pathwaysMicroRNA-22-3pTumor necrosis factorProliferator-activated receptorEnzyme-linked immunosorbentRegulation of CblMiR-22-3pAsthma groupIL-10Asthma attacksHealthy controlsCytokine pathwaysNecrosis factorEstrogen receptorImmune responseESR1 expressionAsthmaControl groupInvolvement of microRNAs
2014
A three‐gene panel that distinguishes benign from malignant thyroid nodules
Zheng B, Liu J, Gu J, Lu Y, Zhang W, Li M, Lu H. A three‐gene panel that distinguishes benign from malignant thyroid nodules. International Journal Of Cancer 2014, 136: 1646-1654. PMID: 25175491, DOI: 10.1002/ijc.29172.Peer-Reviewed Original ResearchAdultAgedBiomarkers, TumorCarbonic AnhydrasesDatasets as TopicDiagnosis, DifferentialDipeptidyl Peptidase 4FemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNeuroendocrine Secretory Protein 7B2PrognosisReproducibility of ResultsThyroid NeoplasmsThyroid NoduleYoung AdultMulticlass classification of sarcomas using pathway based feature selection method
Gu J, Lu Y, Liu C, Lu H. Multiclass classification of sarcomas using pathway based feature selection method. Journal Of Theoretical Biology 2014, 362: 3-8. PMID: 25014475, DOI: 10.1016/j.jtbi.2014.06.038.Peer-Reviewed Original Research
2013
Deep mRNA Sequencing Analysis to Capture the Transcriptome Landscape of Zebrafish Embryos and Larvae
Yang H, Zhou Y, Gu J, Xie S, Xu Y, Zhu G, Wang L, Huang J, Ma H, Yao J. Deep mRNA Sequencing Analysis to Capture the Transcriptome Landscape of Zebrafish Embryos and Larvae. PLOS ONE 2013, 8: e64058. PMID: 23700457, PMCID: PMC3659048, DOI: 10.1371/journal.pone.0064058.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBlastulaCleavage Stage, OvumEmbryonic DevelopmentGastrulaGene Expression ProfilingGene Expression Regulation, DevelopmentalHigh-Throughput Nucleotide SequencingLarvaOligonucleotide Array Sequence AnalysisSequence Analysis, RNATranscription FactorsTranscriptomeWnt Signaling PathwayZebrafishZebrafish ProteinsConceptsTranscriptome dynamicsZebrafish embryonic developmentTranscription factor familyDistinct expression patternsRNA-seq dataGene expression profilesZygotic genomeZebrafish developmentDeep mRNATranscriptome landscapeAverage expression levelEarly gastrulationGene clusterZebrafish embryosEmbryonic developmentTranscriptomic researchFactor familyExpression patternsExpression profilesFunctional pathwaysMolecular eventsEmbryonic stagesGenesCellular mechanismsExpression levels
2010
Analysis of floral transcription factors from Lycoris longituba
He Q, Cui S, Gu J, Zhang H, Wang M, Zhou Y, Zhang L, Huang M. Analysis of floral transcription factors from Lycoris longituba. Genomics 2010, 96: 119-127. PMID: 20406677, DOI: 10.1016/j.ygeno.2010.04.002.Peer-Reviewed Original ResearchConceptsTranscription factorsLycoris longitubaPutative transcription factorSpecific promoter regionsTF familiesTF genesL. longitubaFloral tissuesFlower formPhylogenetic analysisFloral developmentFlower colorGene transcriptionFlowering processTarget genesExpression patternsPromoter regionFlowering phaseReal-time RT-PCRGenesRT-PCRMYBMADSImportant roleTranscription
2009
Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology
Xu T, Gu J, Zhou Y, Du L. Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology. BMC Bioinformatics 2009, 10: 240. PMID: 19653916, PMCID: PMC2731756, DOI: 10.1186/1471-2105-10-240.Peer-Reviewed Original Research